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. 2017 Jul 13;7:5288. doi: 10.1038/s41598-017-05480-0

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© The Author(s) 2017

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Static and dynamic learning within a 2-POST network. (a) Schematic illustration of a perceptron network with a 3 × 3 PRE layer and 2 POSTs, with 18 synapses between the PRE and POST layers. Inhibitory synapses connect the 2 POSTs to reduce the internal potential of one POST when the other POST fires. (b) Patterns submitted during the first phase (top and bottom bars for static learning) and sequence of 4 pattern shifts for the dynamic learning phase. (c,d) Synaptic weights for POST1 and POST2 at the end of each sequence of learning indicated in (b). (e,f) Time evolution of the synaptic weights 1/R for POST1 and POST2 during the 5 phases of the dynamic learning. Pattern weights (red) and background weights (blue) tend to LRS and HRS, respectively.